We describe Data Science as the practice to gain actionable insights from data and building smart applications for real-world problems. It's a deeply practical field that requires collaboration and the ability to iteratively improve a product. Full-stack data scientists are still rare in 2019.

1. Max Pumperla Version 1.0
Data Science
in Practice
Using Deep Learning

2. What you’ll learn
● Three demos to warm up (5 mins)
● Discuss the term Data Science (5 mins)
● Object detection with Deep Learning (15 mins)
● Tackle a realistic use-case (10 mins)
● Summarize and discuss extensions (10 mins)

3. Demo - live at: https://data-science-demo.herokuapp.com/

4. Common ground
● Intuitive computer vision tasks
● Real-time
● Commoditized
● Impossible 5 years ago
● Use machine learning
● In fact: deep learning

5. Deep
Learning
Data Science is the
practice of gaining
actionable insights from data
and building smart products
for real-world problems

6. Cool applications, but...
● Black box, we didn’t build anything
● Actionable insights need clear goals
● At most part of a product
● Not at all real-world
Black box
Web app

7. What’s in the box?
On object detection
● Task: Find bounding boxes for objects in
images + correct labels
● Relatively easy for humans*
● You Only Look Once (YOLO) state of the art
detection algorithm
● Accurate and very fast (~30FPS)
● Other strong alternatives available

8. Machine learning paradigm
Data
Computer Algorithm
Output
Banana
Algorithm
New data
Output
Training Testing
Bicycle
Banana

9. Lightning view on Deep Learning
Deep neural network
Dog
Dog
Input Predict
Learn
Compare (boxes & labels)
1 2
3
4
Update network
weights accordingly
Dog
Dog
Dog
Cat
Dog
Dog

10. Live-coding:
YOLO in practice

11. Data Science Experimentation Flow
Data
Collection
Data
Cleaning
Data
Processing
Model
training
Model
definition
Model
evaluation

12. Use case: Security cameras
● Your company has 10.000 security cameras
● CEO wants gun* detection in real time (tomorrow)
● Dashboard for reporting & alerts
● You’re the lead data scientist on the project

13. Real-time security dashboard demo

14. Our Minimum Viable Product (MVP)
Data
collection
Data
processing
Model
training
AlgorithmWeb cam App
Reporting &
Alerting
1. Iteration
Data Science is the practice of
gaining actionable insights from
data and building smart products
for real-world problems

15. Parallel data processing & training
AlgorithmWeb cam App
Reporting &
Alerting
2. IterationProduction
DBs
Parallel
processing

16. Stream processing & production
App
Robust, low-latency, scales, secure
Reporting &
Alerting
3. Iteration

17. Customer data & insights
App
Robust, low-latency, scales, secure
Reporting &
alerting
System
monitoring
Customer
dashboard
4. Iteration

18. Black Box vs. Complex System
App
Robust, low-latency, scales,
secure
Reporting
& alerting
System
monitoring
Customer
dashboard
Black box
Web app

19. Key Takeaways
● Data science is a complex, practical field - not just experimentation
● Iterative process & team effort
● Machine and deep learning often center stage
● Full-stack data scientists are still rare - your turn!

20. Thank you.
https://data-science-demo.herokuapp.com/
https://www.slideshare.net/MaxPumperla
https://github.com/maxpumperla
Demo:
Slides:
Code: